Some other covid19 visualizations:

https://coronavirus.1point3acres.com/

https://coronavirus.jhu.edu/map.html

# data source https://www.census.gov/data/datasets/time-series/demo/popest/2010s-state-total.html and wikipedia
df_population <- data.frame(
  state = c("AK", "AL", "AR", "AS", "AZ", "CA", "CO", "CT", "DC", "DE", "FL", 
            "GA", "GU", "HI", "IA", "ID", "IL", "IN", "KS", "KY", "LA", "MA", 
            "MD", "ME", "MI", "MN", "MO", "MP", "MS", "MT", "NC", "ND", "NE", 
            "NH", "NJ", "NM", "NV", "NY", "OH", "OK", "OR", "PA", "PR", "RI", 
            "SC", "SD", "TN", "TX", "UT", "VA", "VI", "VT", "WA", "WI", "WV", "WY"),
  population = c(731545, 4903185, 3017804, 55465 , 7278717, 39512223, 5758736, 3565287, 705749, 973764, 21477737,
                 10617423, 165768, 1415872, 3155070, 1787065, 12671821, 6732219, 2913314, 4467673, 4648794, 6892503, 
                 6045680, 1344212,  9986857, 5639632, 6137428, 56882, 2976149, 1068778, 10488084, 762062, 1934408,
                 1359711, 8882190, 2096829, 3080156, 19453561, 11689100, 3956971, 4217737, 12801989, 3193694, 1059361,
                 5148714, 884659, 6829174, 28995881, 3205958, 8535519, 106977 , 623989, 7614893, 5822434, 1792147, 578759)
)

# The Atlantic Monthly Group (CC BY-NC 4.0)
# source: https://covidtracking.com/api

df_states <- fread("https://covidtracking.com/api/v1/states/daily.csv") %>% 
               replace(is.na(.), 0) %>%
               inner_join(df_population, by = "state")%>%
               mutate(date = as.Date(as.character(date), "%Y%m%d"))

tableau10 <- as.list(ggthemes_data[["tableau"]][["color-palettes"]][["regular"]][[1]][,2])$value
first_day <- as.Date("2020-03-15") # to select a date
today <-  as.Date(toString(max(df_states$date)))
  
kable(head(df_states, n = 3))
date state positive probableCases negative pending totalTestResultsSource totalTestResults hospitalizedCurrently hospitalizedCumulative inIcuCurrently inIcuCumulative onVentilatorCurrently onVentilatorCumulative recovered dataQualityGrade lastUpdateEt dateModified checkTimeEt death hospitalized dateChecked totalTestsViral positiveTestsViral negativeTestsViral positiveCasesViral deathConfirmed deathProbable totalTestEncountersViral totalTestsPeopleViral totalTestsAntibody positiveTestsAntibody negativeTestsAntibody totalTestsPeopleAntibody positiveTestsPeopleAntibody negativeTestsPeopleAntibody totalTestsPeopleAntigen positiveTestsPeopleAntigen totalTestsAntigen positiveTestsAntigen fips positiveIncrease negativeIncrease total totalTestResultsIncrease posNeg deathIncrease hospitalizedIncrease hash commercialScore negativeRegularScore negativeScore positiveScore score grade population
2020-10-27 AK 14798 0 562631 0 totalTestsViral 577429 81 0 0 0 8 0 7016 A 10/27/2020 03:59 2020-10-27T03:59:00Z 10/26 23:59 70 0 2020-10-27T03:59:00Z 577429 13245 563865 14798 70 0 0 0 0 0 0 0 0 0 0 0 0 0 2 385 6200 577429 6585 577429 2 0 7e2c10c46a59bf7b86096ce9df6bc962cab5cb74 0 0 0 0 0 0 731545
2020-10-27 AL 186437 26998 1153448 0 totalTestsViral 1312887 1001 19974 0 2035 0 1164 74439 A 10/27/2020 11:00 2020-10-27T11:00:00Z 10/27 07:00 2892 19974 2020-10-27T11:00:00Z 1312887 0 0 159439 2699 193 0 0 0 0 0 63683 0 0 0 0 0 0 1 1115 4455 1339885 5193 1339885 26 0 fcc11dfcba1953aae8cbf7a4a63b867f3a4ec519 0 0 0 0 0 0 4903185
2020-10-27 AR 107679 7618 1203175 0 totalTestsViral 1303236 660 6841 232 0 89 819 96322 A+ 10/27/2020 00:00 2020-10-27T00:00:00Z 10/26 20:00 1857 6841 2020-10-27T00:00:00Z 1303236 0 1203175 100061 1696 161 0 0 0 0 0 0 0 0 49893 8484 21856 3300 5 952 7740 1310854 8391 1310854 24 73 0664df780cf8e6e191b1a381accaf38b459550df 0 0 0 0 0 0 3017804

Rhode Island (as I live in RI now)

df_states %>% filter(state == "RI") %>%
    ggplot() + 
      geom_label(x = first_day, y = 650, color = "darkgray", label = "total positive", size = 2, hjust = 0) + 
      geom_text(mapping = aes(x = date, y = 600, label = positive), color = "darkgray", size = 2, angle = 90, hjust = 0)+ 
      #geom_label(x = first_day, y = 800, color = "black", label = "death", size = 2, hjust = 0) + 
      geom_label(x = first_day, y = 550, color = tableau10[2], label = "positiveIncrease", size = 2, hjust = 0) + 
      geom_label(x = first_day, y = 500, color = tableau10[1], label = "hospitalizedCurrently", size = 2, hjust = 0) + 
      # geom_line(mapping = aes(x = date, y = death), alpha = 0.7, color = "black", size = LINE_SIZE) + 
      # geom_text(mapping = aes(x = date - 0.5, y = death + 10, label = death), color = "black", size = 1.5) + 
      # geom_point(mapping = aes(x = date, y = death), color = "black", shape = 10) + 
      geom_line(mapping = aes(x = date, y = hospitalizedCurrently), alpha = 0.7, color = tableau10[1], size = LINE_SIZE) + 
      geom_text(mapping = aes(x = date - 0.5, y = hospitalizedCurrently + 10, label = hospitalizedCurrently), color =  tableau10[1], size = 1.25) + 
      geom_point(mapping = aes(x = date, y = hospitalizedCurrently), color = tableau10[1], shape = 15) + 
      geom_line(mapping = aes(x = date, y = positiveIncrease), alpha = 0.7, color = tableau10[2], size = LINE_SIZE) + 
      geom_text(mapping = aes(x = date - 0.5, y = positiveIncrease + 10, label = positiveIncrease), color =  tableau10[2], size = 1.25)+ 
      geom_point(mapping = aes(x = date, y = positiveIncrease), color = tableau10[2]) + 
      scale_x_date(limits = c(first_day, today), breaks = seq(first_day, today, by = "day")) + 
      xlab("Date") + ylab("") + ggtitle("RI")

US - all states

df_states %>% group_by(date) %>%
    summarise(positiveIncrease = sum(positiveIncrease), hospitalizedCurrently = sum(hospitalizedCurrently), total = sum(positive)) %>% 
    ungroup() %>%
    ggplot() + 
     geom_label(x = first_day, y = 80000, color = "darkgray", label = "total positive: ", size = 2, hjust = 0) +
     geom_text(mapping = aes(x = date, y = 80000, label = total), color = "darkgray", size = 2, angle = 90, hjust = 0) +
     geom_label(x = first_day, y = 70000, color = tableau10[1], label = "hospitalizedCurrently", size = 2, hjust = 0) +
     geom_label(x = first_day, y = 75000, color = tableau10[2], label = "positiveIncrease", size = 2, hjust = 0) +
     geom_line(mapping = aes(x = date, y = hospitalizedCurrently), alpha = 0.7, color = tableau10[1], size = LINE_SIZE) +
     geom_text(mapping = aes(x = date - 0.5, y = hospitalizedCurrently + 1000, label = hospitalizedCurrently), color =  tableau10[1], size = 1.25) +
     geom_point(mapping = aes(x = date, y = hospitalizedCurrently), color = tableau10[1], shape = 15) +
     geom_line(mapping = aes(x = date, y = positiveIncrease), alpha = 0.7, color = tableau10[2], size = LINE_SIZE) +
     geom_text(mapping = aes(x = date - 0.5, y = positiveIncrease + 1000, label = positiveIncrease), color =  tableau10[2], size = 1.25) +
     geom_point(mapping = aes(x = date, y = positiveIncrease), color = tableau10[2]) +
     scale_x_date(limits = c(first_day, today), breaks = seq(first_day, today, by = "day")) +
     xlab("Date") + ylab("") + ggtitle("US - positiveIncrease & hospitalizedCurrently")

US - daily top-3 contributors

If a state has been a top 3 contributor

as_top <- df_states %>%
    filter(date > first_day)%>%
    mutate(str_date = as.character(date))%>%
    group_by(str_date) %>%
    arrange(positiveIncrease, by_group = TRUE)%>%
    slice_tail(n = 3) %>%
    ungroup() %>%
    summarise(unique(state))
as_top <- unlist(as_top)
    
df_states %>%
    filter(state %in% as_top) %>%
    ggplot() +
      stat_steamgraph(mapping = aes(x = date, y = positiveIncrease, group = state, fill = state))  +
      scale_x_date(limits = c(first_day, today), breaks = seq(first_day, today, by = "week"))  +
      scale_y_continuous(breaks = seq(-20000, 20000, by = 5000), labels = c("20000", "15000", "10000", "5000", "0", "5000", "10000", "15000", "20000")) +
      scale_fill_tableau(palette = "Tableau 20") +
      xlab("Date") + ylab("positiveIncrease") + ggtitle("If a state was a top-3 contributor on a day")

US - positiveIncrease by state

num_lag <- 21

find_coef <- function(x, y){
  m <- lm(y ~ x)
  return(coef(m)[2])
}


df_colors <-  df_states %>%
  group_by(state)%>%
  arrange(date, .by_group = TRUE) %>%
  slice_tail(n = num_lag) %>% # last N days
  summarise(trend_coef = find_coef(date, positiveIncrease)) %>% 
  mutate(trend_color = ifelse(trend_coef > 0, "increasing", ifelse(trend_coef < 0, "decreasing", "stable"))) %>% 
  ungroup()%>%
  replace(is.na(.), 0) %>%
  select(state, trend_coef, trend_color) 
 
  
df_states %>% 
    inner_join(df_colors, by = "state") %>%
    ggplot() +
      geom_smooth(mapping = aes(x = date, y = positiveIncrease), color = "gray", alpha = 0.3, method = "loess", size = LINE_SIZE) +
      geom_line(mapping = aes(x = date, y = positiveIncrease, color = trend_color), alpha = 0.7, size = LINE_SIZE) +
      geom_point(mapping = aes(x = date, y = positiveIncrease, color = trend_color), size = 1) +
      scale_x_date(limits = c(first_day, today), breaks = seq(first_day, today, by = "week")) +
      scale_colour_tableau() +
      facet_wrap(state ~ ., ncol = 6, scales = "free") +
      xlab("Date") + ylab("") + ggtitle("US - positiveIncrease by state, colored by the trend of last 21 days")

df_states %>% 
    inner_join(df_colors, by = "state") %>%
    mutate(positiveIncreasePerMillion = positiveIncrease / population * 1000000)%>%
    ggplot() +
      geom_smooth(mapping = aes(x = date, y = positiveIncreasePerMillion), color = "gray", alpha = 0.3, method = "loess", size = LINE_SIZE) +
      geom_line(mapping = aes(x = date, y = positiveIncreasePerMillion, color = trend_color), alpha = 0.7, size = LINE_SIZE) +
      geom_point(mapping = aes(x = date, y = positiveIncreasePerMillion, color = trend_color), size = 1) +
      scale_y_continuous(limits = c(0, 800), breaks = seq(0, 800, by = 200)) +
      scale_x_date(limits = c(first_day, today), breaks = seq(first_day, today, by = "week")) +
      scale_colour_tableau() +
      facet_wrap(state ~ ., ncol = 6, scales = "free")  +
      xlab("Date") + ylab("") + ggtitle("US - positiveIncreasePerMillion by state, colored by the trend of last 21 days")

US - hospitalizedCurrently by state

df_states %>% 
    ggplot() +
      geom_smooth(mapping = aes(x = date, y = hospitalizedCurrently), color = "gray", alpha = 0.3, method = "loess", size = LINE_SIZE) +
      geom_line(mapping = aes(x = date, y = hospitalizedCurrently), alpha = 0.7, color = tableau10[3], size = LINE_SIZE) +
      geom_point(mapping = aes(x = date, y = hospitalizedCurrently), color = tableau10[3], size = 1) +
      scale_x_date(limits = c(first_day, today), breaks = seq(first_day, today, by = "week")) +
      facet_wrap(state ~ ., ncol = 6, scales = "free") +
      xlab("Date") + ylab("") + ggtitle("US - hospitalizedCurrently by state")

US - dailyTestPositiveRate against overallTestedPopulationRate

df_pr <- df_states %>% 
    mutate(testPositiveRate = positiveIncrease / totalTestResultsIncrease, testedPopulationRate = totalTestResults / population) %>%
    filter(testPositiveRate > 0 & testPositiveRate < 1) # rm buggy data to allow log scales
  
df_pr_colors <-  df_pr %>%
  group_by(state)%>%
  arrange(date, .by_group = TRUE) %>%
  slice_tail(n = num_lag) %>% # last N days
  summarise(trend_coef = find_coef(date, testPositiveRate)) %>% 
  mutate(trend_color = ifelse(trend_coef > 0, "increasing", ifelse(trend_coef < 0, "decreasing", "stable"))) %>% 
  ungroup()%>%
  replace(is.na(.), 0) %>%
  select(state, trend_coef, trend_color) 

df_pr %>%
 inner_join(df_pr_colors, by = "state") %>%
 ggplot() +
    geom_smooth(mapping = aes(x = testedPopulationRate, y = testPositiveRate), color = "gray", alpha = 0.3, method = "loess", size = LINE_SIZE) +
    geom_line(mapping = aes(x = testedPopulationRate, y = testPositiveRate, color = trend_color), alpha = 0.7, size = LINE_SIZE) +
    geom_point(mapping = aes(x = testedPopulationRate, y = testPositiveRate, color = trend_color), size = 1) +
    scale_x_continuous(limits = c(0, 1.2), breaks = seq(0, 1.2, by = 0.05)) +
    scale_y_continuous(limits = c(0.001, 1), trans = 'log10', breaks = c(0.001, 0.01, 0.05, 0.1, 0.2, 0.3, 0.5, 0.75, 1)) +
    scale_colour_tableau() +
    facet_wrap(state ~ ., ncol = 6, scales = "free")  +
    xlab("dailyTestPositiveRate") + ylab("overallTestedPopulationRate") + ggtitle("US - dailyTestPositiveRate against overallTestedPopulationRate")

US - death per 10k by state

df_states %>% 
    mutate(deathPer10K = death / population * 10000) %>%
    ggplot() +
     geom_line(mapping = aes(x = date, y = deathPer10K), alpha = 0.7, color = tableau10[3], size = LINE_SIZE) +
     geom_point(mapping = aes(x = date, y = deathPer10K), color = tableau10[3], size = 1) +
     scale_x_date(limits = c(first_day, today), breaks = seq(first_day, today, by = "week")) +
     scale_y_continuous(limits = c(0, 20), breaks = seq(0, 20, by = 5)) +
     facet_wrap(state ~ ., ncol = 6, scales = "free")  +
     xlab("date") + ylab("death per 10k") + ggtitle("US - death per 10k by state")

US - positive per 1k by state

df_states %>% 
    mutate(positivePerOneK = positive / population * 1000) %>%
    ggplot() +
      geom_line(mapping = aes(x = date, y = positivePerOneK), alpha = 0.7, color = tableau10[4], size = LINE_SIZE) +
      geom_point(mapping = aes(x = date, y = positivePerOneK), color = tableau10[4], size = 1) +
      scale_y_continuous(limits = c(0, 25), breaks = seq(0, 25, by = 5)) +
      scale_x_date(limits = c(first_day, today), breaks = seq(first_day, today, by = "week")) +
      facet_wrap(state ~ ., ncol = 6, scales = "free") +
      xlab("date") + ylab("") + ggtitle("US - positivePerOneK by state")

US - tested amount by state

df_states %>% 
    mutate(testResultsIncrease = positiveIncrease + negativeIncrease) %>%
    ggplot() +
      geom_smooth(mapping = aes(x = date, y = testResultsIncrease), color = "gray", alpha = 0.3, method = "loess", size = LINE_SIZE) +
      geom_line(mapping = aes(x = date, y = testResultsIncrease), alpha = 0.7, color = tableau10[7], size = LINE_SIZE) +
      geom_point(mapping = aes(x = date, y = testResultsIncrease), color = tableau10[7], size = 1) +
      scale_x_date(limits = c(first_day, today), breaks = seq(first_day, today, by = "week")) +
      facet_wrap(state ~ ., ncol = 6, scales = "free")  +
      xlab("date") + ylab("testResultsIncrease") + ggtitle("US - testResultsIncrease by state")